DeepSeek AI: Inside China’s Open-Source Answer to ChatGPT

Deepseek AI Model

DeepSeek AI has pulled off something remarkable. The company built a ChatGPT competitor for just $6 million, while OpenAI reportedly spent $100 million on GPT-4. This Chinese AI company started in July 2023 and has grown faster than anyone expected to become one of the most important players in the global AI world.

The company’s success goes well beyond just saving money. Their AI assistant topped Apple’s App Store charts as the most downloaded free app in January 2025. Users across the US, UK, and Australia downloaded it 1.6 million times. DeepSeek’s models have earned a spot among the top 10 on Chatbot Arena, matching their competitors’ capabilities while running at 20 to 50 times lower operational costs than OpenAI’s solutions.

This piece will take you through DeepSeek’s story from the beginning. We’ll get into their technical setup and see how this innovator revolutionizes the AI industry with their budget-friendly approach to developing artificial intelligence.

DeepSeek’s Journey from Hedge Fund to AI Pioneer

High-Flyer started as a hedge fund in 2016, building on Liang Wenfeng’s trading experience during the 2007-2008 financial crisis. The fund grew impressively and managed investments through AI-driven algorithms, with its portfolio reaching 100 billion yuan.

High-Flyer’s Pivot to AI Development

The company made a major transformation in April 2023 by creating an artificial general intelligence lab separate from its financial operations. Before this change, High-Flyer had invested heavily in computing power. The company built two AI supercomputing clusters with Nvidia A100 chips – a 1,100-chip cluster in 2020 that cost 200 million yuan, and a larger 10,000-chip cluster in 2021 worth 1 billion yuan.

Liang Wenfeng’s Vision for Open Source AI

Liang Wenfeng, who owns 55% of High-Flyer and controls 99% of voting rights, created DeepSeek with a clear vision for open-source AI development. The company thought over its strategy and focused resources on creating models to compete with OpenAI instead of developing apps. DeepSeek took an unusual path under his leadership by hiring diverse talent, including literature majors, to improve AI models.

Key Milestones in DeepSeek’s Growth

DeepSeek’s rapid progress includes these important releases:

  • November 2023: Launch of DeepSeek Coder
  • December 2024: Release of DeepSeek-V3-Base and chat model
  • January 2025: Introduction of DeepSeek-R1 mobile app, which surpassed ChatGPT as the most downloaded free iOS app

On top of that, DeepSeek’s steadfast dedication to open-source principles has promoted innovation despite hardware limitations. The company streamlined processes in development and created competitive models while facing U.S. export restrictions on advanced chips. This approach challenged traditional beliefs about AI development costs and showed that state-of-the-art results don’t always need the latest hardware.

Inside DeepSeek V3’s Technical Architecture

DeepSeek V3’s architecture features an innovative approach to large-scale language processing. The model has 671 billion total parameters and activates 37 billion parameters per token. This represents a major step forward in efficient model design.

Multi-head Latent Attention Design

The Multi-head Latent Attention (MLA) mechanism is the life-blood of DeepSeek V3’s efficiency. MLA compresses attention input into low-dimensional latent vectors and reduces the Key-Value (KV) cache by 93.3%. Down-projection matrices compress a 128-dimension per head into a 512-dimensional latent vector.

The model also uses a decoupled Rotary Positional Embeddings (RoPE) system that applies rotations to a subset of decoupled keys and queries. This design lets the model process sequences up to 128K tokens while keeping memory overhead minimal.

Expert Routing System Implementation

DeepSeek V3 employs a sophisticated Mixture-of-Experts (MoE) framework with:

  • 1 shared expert to handle universal patterns
  • 256 routed experts per layer, with 8 actively selected per token
  • Dynamic bias adjustment system for load balancing

The model uses an auxiliary-loss-free load balancing strategy that prevents routing collapse – where a few experts dominate computation. This approach adjusts expert bias terms based on workload distribution to ensure balanced expert utilization.

Training Infrastructure Overview

The training infrastructure includes 2048 NVIDIA H800 GPUs connected via NVLink for intra-node and InfiniBand for inter-node communication. Several optimization techniques power the system:

The DualPipe algorithm reduces pipeline bubbles through bidirectional scheduling and feeds microbatches from both pipeline ends. The model also uses 16-way Pipeline Parallelism and 64-way Expert Parallelism across 8 nodes.

DeepSeek V3’s architectural innovations deliver remarkable efficiency in both training and inference phases. The model excels especially when managing long sequences and distributing computational load across its expert network.

Cost-Efficient Training Methodology

Recent debates about DeepSeek’s training costs show a more complex financial picture than first reported. The commonly cited AUD 9.17 million figure shows GPU pre-training expenses, but the actual investment is much higher than this amount.

$6M Training Cost Breakdown

The training infrastructure has 2,048 NVIDIA H800 GPUs. In spite of that, this hardware costs between AUD 76.45-100M. The total server capital expenditure reaches about AUD 1.99 billion under these conditions. The company’s GPU inventory has a mix of H800s, H100s, and country-specific H20s from NVIDIA.

The development process needs multiple training iterations and extensive testing. The Multi-Head Latent Attention (MLA) technology reduces inference costs by 93.3% through decreased key-value caching. This technology needed several months of development and substantial GPU hours.

Hardware Optimization Techniques

DeepSeek uses several sophisticated optimization strategies to optimize training:

  • FP8 mixed precision computation to reduce computational costs
  • Memory compression and load balancing techniques
  • PTX programming to control GPU instruction execution better
  • DualPipe algorithm to improve GPU communication

The company’s approach to hardware optimization goes beyond reliable infrastructure. Their mixed-/low-precision computation method cuts down processing overhead. An optimized reward function makes sure compute power goes to high-value training data, which prevents wasting resources on redundant information.

Sparsity techniques help the model predict parameters needed for specific inputs. Memory compression methods and load balancing mechanisms boost the system performance further. These optimizations help DeepSeek achieve remarkable efficiency gains while maintaining model capabilities.

The company manages costs well through its integrated approach to infrastructure optimization. DeepSeek has refined every part of its training pipeline instead of just focusing on raw computing power. This strategy proves that successful AI model training depends on optimizing the entire infrastructure rather than just adding more computational resources.

DeepSeek AI Agent Performance Analysis

Recent Knowledge Observation Group (KOG) tests show DeepSeek’s strong position in the AI world. DeepSeek scored 5.5 out of 6 and beat OpenAI’s o1 and ChatGPT-4o.

Benchmark Comparisons with ChatGPT

DeepSeek’s models show strong results in many evaluation metrics. The model reached impressive scores in these areas:

  • MMLU (Massive Multitask Language Understanding): 90.8% accuracy
  • KOG adversarial tests: 5.5/6 score
  • General knowledge assessment: Matches OpenAI’s o1-1217

The model stayed stable even during tough testing conditions that challenge AI systems.

Mathematical Reasoning Capabilities

DeepSeek’s math skills stand out clearly. Test results show DeepSeek-R1 reached a 79.8% pass rate on AIME 2024 and hit a 97.3% success rate on MATH-500. DeepSeekMath 7B achieved 51.7% accuracy on tough MATH benchmarks without any extra tools.

The model solves problems just like humans think through math. Complex problems make DeepSeek use a step-by-step process. To cite an instance, the model solved math sequence problems by:

  • Spotting wrong approaches
  • Going back when needed
  • Looking at other ways to solve
  • Checking its own answers

Code Generation Accuracy Tests

Software development tests give mixed results for DeepSeek. The model hit 49.2% accuracy on SWE-bench Verified tests, putting it just ahead of OpenAI’s o1-1217 at 48.9%.

DeepSeek-Coder-V2 now works with 338 programming languages, up from 86, and can handle 128K tokens. The model excels at coding challenges and earned a Codeforces rating better than 96.3% of human coders.

Real-life tests show DeepSeek V3 works great at making both user interface and program logic parts. The R1 model has some trouble with edge cases and input checks. The model creates code fast in quick development projects, but needs careful review before going to production.

Real-World Applications and Limitations

DeepSeek AI has found its way into many sectors, but organizations still don’t deal very well with its deployment challenges. Latest analysis shows both its promising features and key limitations in business environments.

Enterprise Use Cases

DeepSeek AI shines at risk assessment and fraud detection in the financial sector through its predictive analytics capabilities. The technology works well in several key industries:

  • Healthcare: Medical image analysis and diagnostic support
  • Education: Individual-specific learning and automated grading systems
  • Finance: Risk management and investment analysis
  • Manufacturing: Quality control and supply chain optimization
  • Retail: Inventory management and demand forecasting

J.P. Morgan’s Athena uses Python-based implementations for risk management. Merative (formerly IBM Watson Health) uses the technology to boost diagnostic procedures. These business deployments show how versatile open-source AI solutions can be in solving complex business challenges.

Content Generation Examples

DeepSeek’s content generation capabilities are remarkably versatile. The system excels at creating various content types, proven by its performance in technical documentation and creative writing tasks. The model stands out in:

  • Technical documentation and API documentation
  • Blog content and marketing materials
  • Code generation across 338 programming languages

The platform knows how to keep context during long interactions, making it valuable for long-form content creation. The system can process up to 64,000 input tokens, which helps create detailed documents while keeping them logically connected.

Current Technical Constraints

Several technical limitations affect how businesses can use DeepSeek. Security testing has exposed vulnerabilities, mainly in prompt leakage and goal hijacking scenarios. The models score lowest among leading systems in cybersecurity tests and need extra safeguards for business use.

Key technical constraints include:

  1. Security Vulnerabilities: The system can be vulnerable to jailbreak attempts and prompt manipulation
  2. Response Time Issues: The R1 model responds slower than V3, sometimes taking several minutes to process queries
  3. Data Privacy Concerns: Questions remain about data handling and storage practices, especially with cross-border data access

LatticeFlow AI’s analysis shows that DeepSeek’s models need major changes to meet enterprise security standards. Businesses using these models must invest in extra security measures. These costs can reach hundreds of thousands of dollars.

The platform’s efficiency-focused design brings some operational trade-offs. The model sometimes has trouble keeping consistent performance during long interactions. The system also has issues handling edge cases and input validation scenarios, so it needs careful implementation in production environments.

Government agencies worry about data sovereignty and security. Australian authorities have put restrictions on government device usage because of data handling concerns. These limits show why robust data protection frameworks matter in business deployments.

Conclusion

DeepSeek AI shows us that groundbreaking AI development doesn’t just need huge budgets. They built competitive AI models for $6 million, while OpenAI spent $100 million. This achievement challenges what we know about AI development costs.

The company’s technical design shines through its Multi-head Latent Attention mechanism and expert routing system. These innovations help DeepSeek match or exceed the measures of well-established companies with substantially lower costs. Their thoughtful approach proves that smart design can overcome hardware constraints.

Real-world use reveals a mixed picture. DeepSeek performs exceptionally well in mathematical reasoning and code generation. However, you need to think over security gaps and response delays before enterprise deployment. These problems are systemic in open-source AI solutions that continue to rise.

DeepSeek’s transformation from a hedge fund to an AI pioneer proves something important. The quickest way to succeed in AI development relies more on smart approaches and resource management than massive investments. Their story points to what a world of AI tools might look like, though security and operational needs remain crucial.

Author

  • Benjamin Paine Headshot

    Managing Director of one of Australia's leading Digital Marketing Agencies... With over 5+ years of hands on experience in SEO, managing both national & international organisations SEO strategy and campaign distribution. Having won several international awards (Search Awards, Clutch, TechBehemoth etc.) for both paid media and search campaign success... He is a front runner in leading search and defining the playbook for the Australian market.

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